Evaluation of Novel Cone-Beam CT for Guidance and Adaptation of Precision Radiotherapy

NCT05176860 | Completed Results FDA Device

• 1 other identifier: VAR-2021-12
• Study Type: interventional
• Target: 31
• Locations: 1 country
• Sites: 1

Brief Summary

This is a feasibility study investigating the image quality of a new, high-performance cone beam CT (CBCT) used for on-couch imaging during radiotherapy treatments.

Conditions

Lung Cancer; Liver Cancer; Breast Cancer; Other Cancer

Outcome Measures

Primary Outcomes (5)

• CBCT Image Quality - Artifact Index

  Artifact Index (AI) is a measurement of the strength of imaging artifact and the degree to which is affects visibility of anatomical structures in the vicinity of the artifact. Artifacts can be produced in CT and CBCT images by a number of factors, such as metal implants, gas, or breathing motion. AI = sqrt((STD\_VOI)\^2 - (STD\_background)\^2), where STD\_VOI is the standard deviation of the image Hounsfield Units in a region of interest at the location of an artifact, and STD\_background is the standard deviation of the Hounsfield Unit values in the background (i.e. in similar tissue but away from the artifact. A lower AI value indicates that the artifact has a lower impact on image quality. Artifacts were identified in all study participants. The median AI across the study population is presented for four imaging modalities.

  1 day

• CBCT Image Quality - Image Nonuniformity

  Nonuniformity (NU) is a measure of the variation of CT image intensity in uniform tissue. NU = (HU\_max - HU\_min)/(HU\_max + HU\_min), where HU\_max and HU\_min are the maximum and minimum Hounsfield Unit values among multiple locations sampled within regions of uniform tissue that were relevant to the anatomy of interest (e.g., a uniform region of breast tissue for patients undergoing breast treatments). A lower NU represents greater uniformity of CT image intensity within a region of interest. Median NU across the study population is presented for four imaging modalities.

  1 day

• CBCT Image Quality - Contrast

  Contrast represents the ability to distinguish between two different regions in a CT image (e.g. to distinguish between two adjacent organs). Contrast = \|HU1 - HU2\| where HU1 and HU2 are the mean HU values in two different 100 mm\^2 ROIs, where the ROIs were located in two different tissue types that were relevant to the site being treated (e.g., in the liver and in perihepatic fat for liver treatments). Higher contrast values indicate that it is easier to distinguish between regions (anatomical structures) in a CT image. Median contrast across the study population is presented for four imaging modalities.

  1 week

• CBCT Image Quality - Contrast to Noise Ratio

  Contrast to Noise Ratio (CNR) measures the ability to distinguish an object or lesion from its background. CNR = \|HU1 - HU2\|/\[0.5 (STD1 + STD2)\] where HU1 and HU2 are the mean Hounsfield Unit values in two different 100 mm\^2 ROIs, where the ROIs were located in two different tissue types that were relevant to the site being treated (e.g., in the liver and in perihepatic fat for liver treatments), and STD1 and STD2 are the standard deviations of the HU values in those same ROIs. A higher CNR makes it easier to distinguish an object from its background. CNR analysis was limited to images with similar imaging dose. Median CNR across all study participants treated for lung cancer are presented for three CBCT modalities.

  1 week

• CBCT Image Quality - HU Similarity to CT Simulation

  The intensity of a pixel in a CT image is a function of its Hounsfield Unit (HU) value. HU is also directly related to the underlying electron density, which means that the pixel value of a CT image can be used directly in the calculation of dose for a prescribed radiation treatment plan. CT simulation scanners produce images with high HU accuracy and are regularly used for radiation treatment planning. Here, we present the difference in HU between CT simulation images and different CBCT images. ΔHU = HU\_CBCT - HU\_CTSim, where HU\_CBCT and HU\_CTSim are mean values among HU averages at 4 reference points in a CBCT image and the corresponding CT simulation image, respectively. The lower the ΔHU, the greater the HU accuracy of the CBCT image, and the greater the likelihood that CBCT imaging can be used for radiation treatment planning. Median ΔHU across the study population are presented for three different tissue types for three CBCT imaging modalities.

  1 week

Secondary Outcomes (6)

• Dosimetry Calculations - Gamma Pass Rate

  1 day

• Dosimetry Calculations - Target DVH Volume Metrics

  1 day

• Dosimetry Calculations - Target DVH Dose Metrics

  1 day

• Dosimetry Calculations - Breast OAR DVH Metrics

  1 day

• Dosimetry Calculations - Lung OAR DVH Metrics

  1 day

Study Arms (1)

High-performance CBCT imaging

EXPERIMENTAL

Two additional study imaging sets are acquired.

Device: CBCT Imaging

Interventions

CBCT Imaging DEVICE

Two research CBCT images will be acquired per subject.

High-performance CBCT imaging

Eligibility Criteria

• Age: 19 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Subject is scheduled for treatment on one of the five TrueBeam platforms at the NS Health QE2 site.
• Subject is receiving radiation therapy using a breath-hold technique (for example, lung, liver and left breast cancers).

You may not qualify if:

• Patient is pregnant or has plans for pregnancy during the period of treatment.
• Patient is unwilling to consent to participating to the study, or for whom informed consent is not possible.

Sponsors & Collaborators

• Varian, a Siemens Healthineers Company: lead

Study Sites (1)

Nova Scotia Health (QEII)

Halifax, Nova Scotia, B3H 2E2, Canada

Location

MeSH Terms

Conditions

Lung Neoplasms; Liver Neoplasms; Breast Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Digestive System Neoplasms; Digestive System Diseases; Liver Diseases; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Results Point of Contact

• Title: Sean Davidson
• Organization: Varian Medical Systems

sean.davidson@varian.com

14379918294

Publication Agreements

• PI is Sponsor Employee: No
• Restriction Type: GT60
• Restrictive Agreement: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SINGLE GROUP
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Model Details: This is a single-site study designed to generate data describing the quality and applicability of on-couch high-performance CBCT imaging for anatomy visualization and radiation treatment dosimetry planning.

Study Record Dates

• First Submitted: December 16, 2021
• First Posted: January 4, 2022
• Study Start: December 20, 2022
• Primary Completion: July 30, 2023
• Study Completion: July 30, 2023
• Last Updated: May 6, 2026
• Results First Posted: May 6, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

CA (1)